Proactive incorporation of unsolicited content into human-to-computer dialogs

ABSTRACT

Methods, apparatus, and computer readable media are described related to automated assistants that proactively incorporate, into human-to-computer dialog sessions, unsolicited content of potential interest to a user. In various implementations, based on content of an existing human-to-computer dialog session between a user and an automated assistant, an entity mentioned by the user or automated assistant may be identified. Fact(s)s related to the entity or to another entity that is related to the entity may be identified based on entity data contained in database(s). For each of the fact(s), a corresponding measure of potential interest to the user may be determined. Unsolicited natural language content may then be generated that includes one or more of the facts selected based on the corresponding measure(s) of potential interest. The automated assistant may then incorporate the unsolicited content into the existing human-to-computer dialog session or a subsequent human-to-computer dialog session.

BACKGROUND

Humans may engage in human-to-computer dialogs with interactive softwareapplications referred to herein as “automated assistants” (also referredto as “chatbots,” “interactive personal assistants,” “intelligentpersonal assistants,” “conversational agents,” etc.). For example,humans (which when they interact with automated assistants may bereferred to as “users”) may provide commands and/or requests usingspoken natural language input (i.e. utterances) which may in some casesbe converted into text and then processed, and/or by providing textual(e.g., typed) natural language input. Automated assistants are typicallyreactive, as opposed to proactive. For example, an automated assistantdoes not proactively obtain and provide specific information ofpotential interest to the user. Consequently, the user must provideinitial natural language input (e.g., spoken or typed) before theautomated assistant will respond with substantive information and/orinitiate one or more tasks on behalf of the user.

SUMMARY

Techniques are described herein for configuring automated assistants toproactively incorporate, into human-to-computer dialog sessions,unsolicited content of potential interest to a user. In someimplementations, an automated assistant configured with selected aspectsof the present disclosure—and/or one or more other components acting incooperation with an automated assistant—may perform such incorporationwhen it determines that in an existing human-to-computer dialog session,the automated assistant has effectively fulfilled its obligations to theuser (e.g., the automated assistant is awaiting further instructions).This can be as simple as the user uttering “Good morning” and theautomated assistant providing a generic response, “Good morning to you.”In such a scenario, the user may likely still be engaged (at leastbriefly) with the human-to-computer dialog session (e.g., a chatbotscreen showing an ongoing transcript of the human-to-computer dialog maystill be open, the user may still be within earshot of an audio outputdevice through which the human-to-computer dialog is implemented, etc.).Accordingly, any unsolicited content that is incorporated into thehuman-to-computer dialog session is likely to be consumed by the user.

In some implementations, the unsolicited content may be presented as aremark (e.g., a natural language statement) that is not directlyresponsive to a user's query (e.g., an aside, a side note, etc.). Insome such implementations, the unsolicited content may be prefaced witha suitable statement that identifies the content as something the userdid not specifically solicit but may otherwise be relevant to thecontext of the current conversation, such as “By the way . . . ”, “Didyou know . . . ”, “As a side note . . . ”, etc. And while perhaps notdirectly responsive to the user's query, the unsolicited content may betangential to the context of the conversation in various ways.

Unsolicited (e.g., tangential) content of potential interest to a userthat is incorporated into a human-to-computer dialog may be selected invarious ways. In some implementations, the content may take the form ofone or more facts that are selected based on one or more entitiesmentioned in the conversation by the user and/or by the automatedassistant. For example, if the user or the automated assistant mentionsa particular celebrity, the automated assistant may incorporate one ormore facts about the celebrity, about another similar celebrity, and soforth. The facts about or otherwise related to the entity may come invarious forms, such as recent news items related to the entity (e.g.,“Did you know<entity> turned 57 last week?,” “By the way, <entity> willbe in your town next month,” “By the way, there has been a recent recallof products from <entity>,” etc.), general facts (e.g., birthday,political affiliation, net worth, piece of trivia, spouse, familymembers, awards, price, availability, etc.), and so forth.

In various implementations, multiple facts about an entity mentioned ina human-to-computer dialog may be determined and then ranked, e.g.,based on so-called “measures of potential interest to the user,” and thehighest ranking fact(s) may be presented to the user. A fact's measureof potential interest may be determined in various ways based on avariety of signals.

In some implementations, a measure of potential interest in a particularfact relating to an entity may be determined based on data associatedwith the user's own user profile. A user profile may be associated witha user account used by the user when operating one or more clientdevices (e.g., forming a coordinated “ecosystem” of client devicesassociated with the user). Various data may be associated with a user'sprofile, such as search history (including patterns detectable fromsearch history), messaging history (e.g., including pasthuman-to-computer dialogs between the user and an automated assistant),personal preferences, browsing history, sensor signals (e.g., GlobalPositioning System, or “GPS,” location), and so forth.

Suppose that when a user searches for celebrities (or other publicpersons), the user often also tends to search for the celebrities'political affiliations. Based on detecting this pattern of searching,the automated assistant may assign a relatively large measure ofpotential interest to a political affiliation of a celebrity (or otherpublic person) that is mentioned in a current human-to-computer dialogbetween the automated assistant and the user. In other words, theautomated assistant may generalize the user's particular interest inpolitical affiliations of particular celebrities searched by the user topolitical affiliations of all celebrities.

As another example, suppose that when researching flights (e.g., byengaging the automated assistant and/or by operating a web browser), theuser tends to search flights from the nearest airport, as well as aflights that depart from a different airport somewhat further away,e.g., to compare prices. This search pattern may be detected by theautomated assistant. Suppose the user later asks the automated assistantto provide a price for a flight from the nearest airport to a particulardestination. By default, the automated assistant may directly respond tothe user's query by providing prices for flights from the nearestairport. However, based on the detected searching pattern, the automatedassistant may provide unsolicited content that includes one or moreprices for flights to the destination from the different airport that issomewhat further away.

In some implementations, a measure of potential interest in a particularfact related to a mentioned entity (or a related entity) may bedetermined based on aggregate data generated by, or behavior of, aplurality of users of which the user may or may not be a member. Forexample, suppose users in general tend to search for a particular factabout an entity after searching for the entity itself. That aggregatepattern may be detected and used by automated assistants to determinemeasures of potential interest in facts about entities currently beingdiscussed in human-to-computer dialogs with individual users.

In some implementations, one or more corpuses of “online conversations”between people (e.g., message exchange threads, comment threads, etc.)may be analyzed to detect, for instance, references to entities, as wellas references to facts related to the referenced entities. For example,suppose an entity (e.g., a person) is being discussed in a commentthread, and a participant mentions a particular fact about the entity(e.g., the entity's political affiliation) within a particular proximityof the reference to the entity (e.g., in the same thread, within x daysof, in the same forum, etc.). In some implementations, that fact relatedto that entity (which in some cases may be confirmed against a knowledgegraph containing entities and verified facts) may be flagged orotherwise indicated (e.g., in the same knowledge graph) as being ofpotential interest when that entity is discussed in subsequenthuman-to-computer dialogs. If multiple participants in multipledifferent online conversations tend to mention the same fact when thesame entity is referenced, then that fact's measure of potentialinterest may be increased even further.

As was the case above, the relationship between the particular fact andthe particular entity may be generalized so that similar facts aboutdifferent entities may be assigned measures of potential interestaccordingly. For example, suppose online conversation participantsfrequently mention, e.g., as asides, net worth's of celebrities beingdiscussed. The notion that celebrities' net worth's are frequentlymentioned as asides or side notes when discussing celebrities may beused to determine a measure of potential interest in a net worth of acelebrity (or other public person) currently being discussed in ahuman-to-computer dialog between a user and an automated assistant.

Unsolicited content as described herein is not limited to facts about aparticular entity mentioned in a human-to-computer dialog between a userand an automated assistant. In some implementations, other entities thatare related to the mentioned entity may also be considered. These otherentities may include, for instance, entities that share one or moreattributes with the mentioned entity (e.g., musician, artist, actor,athlete, restaurant, point of interest, etc.), or that share one or moreattributes (e.g., located nearby, temporally proximate events, etc.).For example, suppose a user asks an automated assistant a question abouta particular actor. After answering the user's question, the automatedassistant may incorporate an unsolicited fact about another actor (or adirector the actor has worked with, etc.) into the conversation. Asanother example, suppose a user engages automated assistant 120 to booka reservation at a restaurant. Automated assistant may proactivelyrecommend another restaurant nearby as an alternative, e.g., because itis less expensive, has better reviews, is less likely to be crowded,etc. As yet another example, suppose a user likes two musicians (as maybe determined, for instance, from playlists associated with the user'sprofile or from aggregate user playback histories/playlists). Supposefurther that the user asks when one of the two musicians will be touringnearby soon. After answering the user's question, the automatedassistant may determine that the other musician will be touring nearbysoon, and may incorporate that unsolicited fact into thehuman-to-computer dialog.

Incorporating unsolicited content of potential interest to a user into ahuman-to-computer dialog session may have several technical advantages.The automated assistant may appear more “lifelike” or “human” to theuser, which may incentivize increased interaction with the automatedassistant. Also, the incorporated content is likely to be interesting tothe user because it is selected based on one or more entities (e.g.,people, places, things that are documented in one or more databases,such as a knowledge graph) mentioned by the user or by the automatedassistant during a human-to-computer dialog. Consequently, a user may berelieved of affirmatively soliciting such information, which mayconserve computing resources that otherwise would be used to process theuser's natural language input. Additionally, the user may receivepotentially helpful or interesting information that it may not otherwisehave occurred to the user to solicit.

In some implementations, a method performed by one or more processors isprovided that includes: identifying, based on content of an existinghuman-to-computer dialog session between a user and an automatedassistant, an entity mentioned by the user or automated assistant;identifying, based on entity data contained in one or more databases,one or more facts related to the entity or to another entity that isrelated to the entity; determining, for each of the one or more facts, acorresponding measure of potential interest to the user; generatingunsolicited natural language content, wherein the unsolicited naturallanguage content includes one or more of the facts selected based on thecorresponding one or more measures of potential interest; andincorporating, by the automated assistant into the existinghuman-to-computer dialog session or a subsequent human-to-computerdialog session, the unsolicited natural language content.

These and other implementations of technology disclosed herein mayoptionally include one or more of the following features.

In various implementations, the determining may be based on dataassociated with a user profile associated the user. In variousimplementations, the data associated with the user profile may include asearch history associated with the user. In various implementations, thedetermining may be based on analysis of a corpus of one or more onlineconversations between one or more people. In various implementations,the analysis may include detection of one or more references to one ormore entities, as well as one or references to facts related to the oneor more entities within a particular proximity of the one or morereferences to the one or more entities. In various implementations, theone or more entities may include the entity mentioned by the user orautomated assistant. In various implementations, the one or moreentities may share an entity class with the entity mentioned by the useror automated assistant. In various implementations, the one or moreentities may share one or more attributes with the entity mentioned bythe user or automated assistant.

In various implementations, the determining may include detecting that agiven fact of the one or more facts has been previously referenced inthe existing human-to-computer dialog between the user and the automatedassistant or in a previous human-to-computer dialog between the user andthe automated assistant. In various implementations, the measure ofpotential interest determined for the given fact may reflect thedetecting.

In various implementations, the method may further include eliminating agiven fact of the one or more facts from consideration based ondetecting that the given fact has been previously referenced in theexisting human-to-computer dialog between the user and the automatedassistant or in a previous human-to-computer dialog between the user andthe automated assistant.

In various implementations, the entity may be a location, and a givenfact of the one or more facts may be an event occurring at or near thelocation. In various implementations, the entity may be a person, and agiven fact of the one or more facts may be an upcoming event involvingthe person.

In addition, some implementations include one or more processors of oneor more computing devices, where the one or more processors are operableto execute instructions stored in associated memory, and where theinstructions are configured to cause performance of any of theaforementioned methods. Some implementations also include one or morenon-transitory computer readable storage media storing computerinstructions executable by one or more processors to perform any of theaforementioned methods.

It should be appreciated that all combinations of the foregoing conceptsand additional concepts described in greater detail herein arecontemplated as being part of the subject matter disclosed herein. Forexample, all combinations of claimed subject matter appearing at the endof this disclosure are contemplated as being part of the subject matterdisclosed herein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an example environment in whichimplementations disclosed herein may be implemented.

FIGS. 2, 3, 4, 5, 6, 7, and 8 depict example dialogs between varioususers and automated assistants, in accordance with variousimplementations.

FIG. 9 depicts a flowchart illustrating an example method according toimplementations disclosed herein.

FIG. 10 illustrates an example architecture of a computing device.

DETAILED DESCRIPTION

Now turning to FIG. 1, an example environment in which techniquesdisclosed herein may be implemented is illustrated. The exampleenvironment includes a plurality of client computing devices 1061-N andan automated assistant 120. Although automated assistant 120 isillustrated in FIG. 1 as separate from the client computing devices 106_(1-N), in some implementations all or aspects of automated assistant120 may be implemented by one or more of the client computing devices106 _(1-N). For example, client device 106 ₁ may implement one instanceof or more aspects of automated assistant 120 and client device 106 _(N)may also implement a separate instance of those one or more aspects ofautomated assistant 120. In implementations where one or more aspects ofautomated assistant 120 are implemented by one or more computing devicesremote from client computing devices 106 _(1-N), the client computingdevices 106 _(1-N) and those aspects of automated assistant 120 maycommunicate via one or more networks such as a local area network (LAN)and/or wide area network (WAN) (e.g., the Internet).

The client devices 106 _(1-N) may include, for example, one or more of:a desktop computing device, a laptop computing device, a tabletcomputing device, a mobile phone computing device, a computing device ofa vehicle of the user (e.g., an in-vehicle communications system, anin-vehicle entertainment system, an in-vehicle navigation system), astandalone interactive speaker, and/or a wearable apparatus of the userthat includes a computing device (e.g., a watch of the user having acomputing device, glasses of the user having a computing device, avirtual or augmented reality computing device). Additional and/oralternative client computing devices may be provided. In someimplementations, a given user may communicate with automated assistant120 utilizing a plurality of client computing devices that collectivelyfrom a coordinated “ecosystem” of computing devices. In some suchimplementations, automated assistant 120 may be considered to “serve”that given user, e.g., endowing automated assistant 120 with enhancedaccess to resources (e.g., content, documents, etc.) for which access iscontrolled by the “served” user. However, for the sake of brevity, someexamples described in this specification will focus on a user operatinga single client computing device 106.

Each of the client computing devices 106 _(1-N) may operate a variety ofdifferent applications, such as a corresponding one of a plurality ofmessage exchange clients 107 _(1-N). Message exchange clients 107 _(1-N)may come in various forms and the forms may vary across the clientcomputing devices 106 _(1-N) and/or multiple forms may be operated on asingle one of the client computing devices 106 _(1-N). In someimplementations, one or more of the message exchange clients 107 _(1-N)may come in the form of a short messaging service (“SMS”) and/ormultimedia messaging service (“MMS”) client, an online chat client(e.g., instant messenger, Internet relay chat, or “IRC,” etc.), amessaging application associated with a social network, a personalassistant messaging service dedicated to conversations with automatedassistant 120, and so forth. In some implementations, one or more of themessage exchange clients 107 _(1-N) may be implemented via a webpage orother resources rendered by a web browser (not depicted) or otherapplication of client computing device 106.

As described in more detail herein, automated assistant 120 engages inhuman-to-computer dialog sessions with one or more users via userinterface input and output devices of one or more client devices 106_(1-N). In some implementations, automated assistant 120 may engage in ahuman-to-computer dialog session with a user in response to userinterface input provided by the user via one or more user interfaceinput devices of one of the client devices 106 _(1-N). In some of thoseimplementations, the user interface input is explicitly directed toautomated assistant 120. For example, one of the message exchangeclients 107 _(1-N) may be a personal assistant messaging servicededicated to conversations with automated assistant 120 and userinterface input provided via that personal assistant messaging servicemay be automatically provided to automated assistant 120. Also, forexample, the user interface input may be explicitly directed toautomated assistant 120 in one or more of the message exchange clients107 _(1-N) based on particular user interface input that indicatesautomated assistant 120 is to be invoked. For instance, the particularuser interface input may be one or more typed characters (e.g.,@AutomatedAssistant), user interaction with a hardware button and/orvirtual button (e.g., a tap, a long tap), an oral command (e.g., “HeyAutomated Assistant”), and/or other particular user interface input. Insome implementations, automated assistant 120 may engage in a dialogsession in response to user interface input, even when that userinterface input is not explicitly directed to automated assistant 120.For example, automated assistant 120 may examine the contents of userinterface input and engage in a dialog session in response to certainterms being present in the user interface input and/or based on othercues. In many implementations, automated assistant 120 may engageinteractive voice response (“IVR”), such that the user can uttercommands, searches, etc., and the automated assistant may utilizenatural language processing and/or one or more grammars to convert theutterances into text, and respond to the text accordingly.

Each of the client computing devices 106 _(1-N) and automated assistant120 may include one or more memories for storage of data and softwareapplications, one or more processors for accessing data and executingapplications, and other components that facilitate communication over anetwork. The operations performed by one or more of the client computingdevices 106 _(1-N) and/or by automated assistant 120 may be distributedacross multiple computer systems. Automated assistant 120 may beimplemented as, for example, computer programs running on one or morecomputers in one or more locations that are coupled to each otherthrough a network.

Automated assistant 120 may include a natural language processor 122 anda responsive content engine 130. In some implementations, one or more ofthe engines and/or modules of automated assistant 120 may be omitted,combined, and/or implemented in a component that is separate fromautomated assistant 120. Automated assistant 120 may engage inhuman-to-computer dialog sessions with one or more user(s), viaassociated client devices 106 _(1-N), to provide responsive contentgenerated and/or maintained by responsive content engine 130.

In some implementations, responsive content engine 130 generatesresponsive content in response to various inputs generated by a user ofone of the client devices 106 _(1-N) during a human-to-computer dialogsession with automated assistant 120. The responsive content engine 130provides the responsive content (e.g., over one or more networks whenseparate from a client device of a user) for presentation to the user aspart of the dialog session. For example, responsive content engine 130may generate responsive content in in response to free-form naturallanguage input provided via one of the client devices 106 _(1-N). Asused herein, free-form input is input that is formulated by a user andthat is not constrained to a group of options presented for selection bythe user.

As used herein, a “dialog session” may include alogically-self-contained exchange of one or more messages between a userand automated assistant 120 (and in some cases, other human participantsin the thread). Automated assistant 120 may differentiate betweenmultiple dialog sessions with a user based on various signals, such aspassage of time between sessions, change of user context (e.g.,location, before/during/after a scheduled meeting, etc.) betweensessions, detection of one or more intervening interactions between theuser and a client device other than dialog between the user and theautomated assistant (e.g., the user switches applications for a while,the user walks away from then later returns to a standalonevoice-activated product), locking/sleeping of the client device betweensessions, change of client devices used to interface with one or moreinstances of automated assistant 120, and so forth.

In some implementations, when automated assistant 120 provides a promptthat solicits user feedback, automated assistant 120 may preemptivelyactivate one or more components of the client device (via which theprompt is provided) that are configured to process user interface inputto be received in response to the prompt. For example, where the userinterface input is to be provided via a microphone of the client device106 ₁, automated assistant 120 may provide one or more commands tocause: the microphone to be preemptively “opened” (thereby preventingthe need to hit an interface element or speak a “hot word” to open themicrophone), a local speech to text processor of the client device 106 ₁to be preemptively activated, a communications session between theclient device 106 ₁ and a remote speech to text processor to bepreemptively established, and/or a graphical user interface to berendered on the client device 106 ₁ (e.g., an interface that includesone or more selectable elements that may be selected to providefeedback). This may enable the user interface input to be providedand/or processed more quickly than if the components were notpreemptively activated.

Natural language processor 122 of automated assistant 120 processesnatural language input generated by users via client devices 106 _(1-N)and may generate annotated output for use by one or more othercomponents of automated assistant 120, such as responsive content engine130. For example, the natural language processor 122 may process naturallanguage free-form input that is generated by a user via one or moreuser interface input devices of client device 106 ₁. The generatedannotated output includes one or more annotations of the naturallanguage input and optionally one or more (e.g., all) of the terms ofthe natural language input.

In some implementations, the natural language processor 122 isconfigured to identify and annotate various types of grammaticalinformation in natural language input. For example, the natural languageprocessor 122 may include a part of speech tagger configured to annotateterms with their grammatical roles. For example, the part of speechtagger may tag each term with its part of speech such as “noun,” “verb,”“adjective,” “pronoun,” etc. Also, for example, in some implementationsthe natural language processor 122 may additionally and/or alternativelyinclude a dependency parser (not depicted) configured to determinesyntactic relationships between terms in natural language input. Forexample, the dependency parser may determine which terms modify otherterms, subjects and verbs of sentences, and so forth (e.g., a parsetree)—and may make annotations of such dependencies.

In some implementations, the natural language processor 122 mayadditionally and/or alternatively include an entity tagger (notdepicted) configured to annotate entity references in one or moresegments such as references to people (including, for instance, literarycharacters, celebrities, public figures, etc.), organizations, locations(real and imaginary), and so forth. In some implementations, data aboutentities may be stored in one or more databases, such as in a knowledgegraph 124.” In some implementations, knowledge graph 124 may includenodes that represent known entities (and in some cases, entityattributes), as well as edges that connect the nodes and representrelationships between the entities. For example, a “banana” node may beconnected (e.g., as a child) to a “fruit” node,” which in turn may beconnected (e.g., as a child) to “produce” and/or “food” nodes. Asanother example, a restaurant called “Hypothetical Café” may berepresented by a node that also includes attributes such as its address,type of food served, hours, contact information, etc. The “HypotheticalCafé” node may in some implementations be connected by an edge (e.g.,representing a child-to-parent relationship) to one or more other nodes,such as a “restaurant” node, a “business” node, a node representing acity and/or state in which the restaurant is located, and so forth.

The entity tagger of the natural language processor 122 may annotatereferences to an entity at a high level of granularity (e.g., to enableidentification of all references to an entity class such as people)and/or a lower level of granularity (e.g., to enable identification ofall references to a particular entity such as a particular person). Theentity tagger may rely on content of the natural language input toresolve a particular entity and/or may optionally communicate with aknowledge graph or other entity database to resolve a particular entity.

In some implementations, the natural language processor 122 mayadditionally and/or alternatively include a coreference resolver (notdepicted) configured to group, or “cluster,” references to the sameentity based on one or more contextual cues. For example, thecoreference resolver may be utilized to resolve the term “there” to“Hypothetical Café” in the natural language input “I liked HypotheticalCafé last time we ate there.”

In some implementations, one or more components of the natural languageprocessor 122 may rely on annotations from one or more other componentsof the natural language processor 122. For example, in someimplementations the named entity tagger may rely on annotations from thecoreference resolver and/or dependency parser in annotating all mentionsto a particular entity. Also, for example, in some implementations thecoreference resolver may rely on annotations from the dependency parserin clustering references to the same entity. In some implementations, inprocessing a particular natural language input, one or more componentsof the natural language processor 122 may use related prior input and/orother related data outside of the particular natural language input todetermine one or more annotations.

As mentioned above, automated assistant 120, e.g., by way of responsivecontent engine 130, may utilize one or more resources in generatingsuggestions and/or other unsoliciated content to provide during ahuman-to-computer dialog session with a user of one of the clientdevices 106 _(1-N). In various implementations, the responsive contentengine 130 may include an action module 132, an entity module 134, and aproactive content module 136.

The action module 132 of the responsive content engine 130 utilizesnatural language input received from client computing devices 106_(1-N), and/or annotations of natural language input provided by naturallanguage processor 122, to determine at least one action that isresponsive to the natural language input. In some implementations, theaction module 132 may determine an action based on one or more termsincluded in the natural language input. For example, the action module132 may determine an action based on the action being mapped, in onemore computer readable media, to one or more terms included in thenatural language input. For instance, an action of “add <item> to myshopping list” may be mapped to one or more terms such as “I need <item>from the market . . . ,” “I need to pick up <item>,” “we're out of<item>,” etc.

Entity module 134 may be configured to identify, based on content of anexisting human-to-computer dialog session between a user and automatedassistant 120 (and/or annotations thereof), an entity mentioned by theuser or automated assistant 120. This content may include input providedby one or more users via user interface input device(s) during ahuman-to-computer dialog session between the user(s) and automatedassistant 120, as well as content incorporated into the dialog sessionby automated assistant 120. The entity module 134 may utilize one ormore resources in identifying referenced entities and/or in refiningcandidate entities. For example, the entity module 134 may utilize thenatural language input itself, annotations provided by natural languageprocessor 122, and/or information from knowledge graph 124. In somecases, entity module 134 may be integral with, e.g., the same as, theaforementioned entity tagger forming part of natural language processor122.

Proactive content module 136 may be configured to proactivelyincorporate unsolicited content of potential interest to a user intohuman-to-computer dialog sessions. In some implementations, proactivecontent module 136 may determine—e.g., based on data received from othermodules, such as natural language processor 122, action module 132,and/or entity module 134—that in an existing human-to-computer dialogsession between a user and automated assistant 120, automated assistant120 has responded to all natural language input received from the userduring the human-to-computer dialog session. Suppose a user operatesclient device 106 to request a search for particular information, andthat automated assistant 120 performs the search (or causes the searchto be performed) and returns responsive information as part of thehuman-to computer-dialog. At this point, unless the user has alsorequested other information, automated assistant 120 has fully respondedto the user's request. In some implementations, proactive content module136 may wait for some predetermined time interval (e.g., two seconds,five seconds, etc.) for automated assistant 120 to receive additionaluser input. If none is received during the time interval, proactivecontent module 136 may determine that it has responded to all naturallanguage input received from the user during the human-to-computerdialog session, and that it is now free to incorporate unsolicitedcontent.

Based on one or more entities identified by entity module 134 as beingmentioned in a human-to-computer dialog session (or related thereto),proactive content module 136 may be configured identify, based on entitydata contained in one or more databases (e.g., knowledge graph 124), oneor more facts related to the entity or to another entity that is relatedto the entity. Proactive content module 136 may then determine, for eachof the one or more facts, a corresponding measure of potential interestto the user. Based on the one or more measures of potential interestcorresponding to the one or more facts, proactive content module 136 mayselect one or more of the facts to be included in unsolicited naturallanguage content it generates. Proactive content module 136 may thenincorporate the unsolicited natural language content into the existinghuman-to-computer dialog session or a subsequent human-to-computerdialog session.

In some implementations, measures of potential interest in factsrelating to entities may be determined, e.g., by proactive contentmodule 136, based on data obtain from one or more user profile databases126. Data contained in user profiles database 126 may relate to userprofiles associated with human participants in human-to-computerdialogs. In some implementations, each user profile may be associatedwith a user account used by the user when operating one or more clientdevices. Various data may be associated with a user's profile (and hencestored in user profiles database 126), such as search history (includingpatterns detectable from search history), messaging history (e.g.,including past human-to-computer dialogs between the user and anautomated assistant), personal preferences, browsing history, and soforth. Other information associated with individual user profiles mayalso be stored in, or may be determined based on data stored in, userprofiles database 126. This other user-related information may include,for example, topics of interest to users (which may be stored directlyin database 126 or determined from other data stored therein), searchhistory, browsing history, user-set preferences, current/past locations,media playing history, travel history, content of past human-to-computerdialog sessions, and so forth.

Thus, in some implementations, proactive content module 136 may haveaccess to various signals or other data from one or more client devices106 operated by a user, e.g., directly from the client devices 106,directly from user profiles 126, and/or indirectly via one or morecomputing systems operating as a so-called “cloud.” Topics of interestto a user may include, for instance, particular hobbies (e.g., golfing,skiing, gaming, painting, etc.), literature, movies, musical genres,particular entities (e.g., artists, athletes, sports teams, companies),etc. Other information that may be associated with a user's profile mayinclude, for instance, age, scheduled events of the user (e.g., asdetermined from one or more calendar entries), and so forth.

In various implementations, proactive content module 136 may beconfigured to generate unsolicited content that is indicative of (e.g.,includes) the facts of potential interest to the user, and incorporatethe unsolicited content into a human-to-computer dialog. Thisunsolicited content may come in various forms that may be incorporatedinto an existing human-to-computer dialog session. For example, in someimplementations in which the user is interacting with automatedassistant 120 using a text-based message exchange client 107, theunsolicited content generated by proactive content module 136 may takethe form of text, images, video, or any combination thereof, that may beincorporated into a transcript of the human-to-computer dialog renderedby message exchange client 107. In some implementations, the unsolicitedcontent may include or take the form of a so-called “deep link” that isselectable by the user to expose a different application interface tothe user. For example, a deep link may, when selected by a user, causethe client device 106 to launch (or make active) a particularapplication in a particular state. In other implementations in which theuser is interacting with automated assistant 120 using a speechinterface (e.g., when automated assistant 120 operates on a standaloneinteractive speaker, or on an in-vehicle system), the unsolicitedcontent may take the form of natural language output that is provided tothe user audibly. As noted above, in many cases, the unsolicited contentmay be prefaced by language such as “By the way,” “Did you know,” “As anaside,” etc.

In some implementations, the incorporation of the unsolicited contentmay be performed in response to the determination, e.g., by proactivecontent module 136, that automated assistant 120 has responded to allnatural language input received from the user during thehuman-to-computer dialog session. In some implementations, one or moreof the other operations described above with respect to proactivecontent module 136 may also be performed in response to such an event.Or, in some implementations, those operations may be performed byproactive content module 136 periodically or continuously so thatproactive content module 136 (and hence, automated assistant 120)remains “primed” to quickly incorporate unsolicited content of potentialinterest to a user into a human-to-computer dialog session.

Proactive content module 136 may also have access to (e.g., obtain factsfrom) components other than the user's profile, such as a fresh contentmodule 138 and one or more miscellaneous domain modules 140. Freshcontent module 138 may provide proactive content module 136 with accessto data relating to current events, news, current schedules (e.g.,performer tour dates), current prices (e.g., for goods or services),trending news/searches (e.g., indicated by so-called “hash tags”), andso forth. In some implementations, fresh content module 138 may be partof a larger search engine system and may be configured to return searchresults that are temporally relevant. For example, some search engineinterfaces include a “news” filter that a user can select to limitsearch results to information published by various news sources. Freshcontent module 138 may have access to such information. Miscellaneousdomain modules 140 may provide data from various other domains, and thusmay operate similarly to other search engine filters. For example, a“weather” domain module may return facts related to weather, a “history”domain module may return data related to historical facts, a “trivia”module may return random facts related to entities, and so forth. Insome implementations, a “manual” facts module may be configured toreceive manually-input facts, e.g., from paid advertisers, along withindications of entities related to those facts.

In some implementations, when entity module 134 identifies one or moreentities mentioned during a human-to-computer dialog, proactive contentmodule 136 may draw various facts related to those one or more entitiesfrom one or more sources, such as knowledge graph 124, or one or moremodules 138-140. Proactive content module 136 may then rank the returnedfacts, e.g., by determining the aforementioned measures of user interestassociated with the facts.

Measures of potential user interest in the facts may be determined byproactive content module 136 in various ways based on variousinformation. As noted above, in some implementations, proactive contentmodule 136 may determine measures of potential user interest in factsbased on individual user information, e.g., contained in user profiledatabase 126. For example, if a particular user tends to search for amusician's upcoming tour dates whenever the user also searches forinformation about the musician, then any facts related to upcoming tourdates may be assigned relatively high measures of potential userinterest. In some implementations, facts related to tour dates near theuser's location (e.g., determined using GPS) may be assigned even highermeasures of potential interest than facts relating to faraway tourdates.

In some implementations, proactive content module 136 may generatemeasures of potential user interest in facts based on aggregate dataassociated with searches and/or behavior of a population of users. Ifusers at large tend to search for upcoming tour dates when researchingmusicians, then for similar reasons as above, facts relating to upcomingtour dates, especially those near the user, may be assigned relativelylarge measures of potential user interest when a musician is mentionedin a human-to-computer dialog. If users in general tend to search forrental cars contemporaneously with searching for flight reservations,then facts relating to rental cars (e.g., prices, availability) may beassigned relatively large measures of potential user interest when oneor more flights are mentioned in a human-to-computer dialog. Ifparticipants in online conversations frequently mention a particularproduct's reliability when discussing the product, the facts relating tothe products' reliability may be assigned relatively large measures ofpotential user interest when the product is mentioned in ahuman-to-computer dialog. And so on.

FIG. 2 illustrates an example of a human-to-computer dialog sessionbetween user 101 and an instance of automated assistant (120 in FIG. 1,not depicted in FIG. 2). FIGS. 2-5 illustrate examples of dialogsessions that may occur, via the microphone(s) and speaker(s), between auser 101 of a computing device 210 (depicted as a standalone interactivespeaker but this is not meant to be limiting) and automated assistant120 according to implementations described herein. One or more aspectsof automated assistant 120 may be implemented on the computing device210 and/or on one or more computing devices that are in networkcommunication with the computing device 210.

In FIG. 2, the user 101 provides natural language input 280 of “How manyconcertos did Mozart compose?” in a human-to-computer dialog sessionbetween the user 101 and automated assistant 120. In response to thenatural language input 280, automated assistant 120 provides responsivenatural language output 282 of “Mozart's concertos for piano andorchestra are numbered from 1 to 27.” Rather than waiting for additionaluser input, automated assistant 120 (e.g., by way of proactive contentmodule 136) may proactively incorporate additional content of potentialinterest to the user into the human-to-computer dialog of FIG. 2. Forexample, automated assistant 120 may obtain/receive one or more factsfrom various sources (e.g., 124, 138-140) relating to the entity“Mozart.” Automated assistant 120 proactively incorporates the followingunsolicited content (shown in italics in FIG. 2 and other figures) intothe human-to-computer dialog: “By the way, did you know the LouisvilleOrchestra will be playing a concert next month featuring Mozartcompositions?” Other facts that could have been presented include, butare not limited to, Mozart's date of birth, birthplace, date of death,etc. For this example, it is possible that the upcoming performance wasselected, e.g., by proactive content module 136, because user 101 tendsto search for opportunities to see performances of musical works bymusician he or she searches.

FIG. 3 depicts another example dialog between the user 101 and automatedassistant 120 operating on the computing device 210 during a differentsession. At 380, the user 101 speaks the phrase, “What's the name of myhotel in Louisville?” After searching various data sources (e.g.,calendar entries, travel itinerary emails, etc.) associated with a userprofile of user 101, at 382, automated assistant 120 may reply, “You arestaying at the Brown Hotel.” Automated assistant 120 (e.g., by way ofproactive content module 136) may then determine that it has fullyresponded to the user's natural language input. Accordingly, based on aninterest of user 101 in the flight demonstration squadron known as the“Blue Angels,” and a determination that the Blue Angels will beperforming in Louisville (which is a location entity) soon, automatedassistant 120 may proactively incorporate the following unsolicitedcontent into the human-to-computer dialog: “By the way, during your stayin Louisville, the Blue Angels will be performing. Should I look fortickets?” If user 101 responds in the affirmative, then automatedassistant 120 may, for instance, engage user 101 with additional dialogto procure tickets to the event.

FIG. 4 depicts another example dialog between the user 101 and automatedassistant 120 operating on the computing device 210 during a differentsession. In this example, at 480, user 101 provides the natural languageinput, “What's the name of <performing_artist's> first album?” Afterperforming any necessary searching, automated assistant 120 may respondat 482 with the “<name_of_studio_album>.” (words in <brackets> aremerely meant to generically indicate an entity). Automated assistant 120also determines various facts about the entity and after assigningmeasures potential user interest in the facts and ranking them,automated assistant 120 proactively incorporates the unsolicitedcontent, “By the way, <performance_artist> will be playing close by nextmonth. Do you want more information about tickets?”

In some implementations, an entity about which proactive content module136 determines facts of potential interest need not necessarily bementioned explicitly in a human-to-computer dialog. In someimplementations, this proactively incorporated content may bedetermined, for instance, based on a state of an application operatingon a client device. Suppose user 101 is playing a game on a clientdevice. Automated assistant 120 on computing device 210 may determinethat the other client device is in a particular game-playing state, andmay provide various unsolicited content of potential interest to theuser, such as tips, tricks, recommendations of similar games, etc., aspart of a human-to-computer dialog. In some implementations in whichcomputing device 210 is a standalone interactive speaker, computingdevice 210 may even output background music (e.g., duplicating or addingbackground music) and/or sound effects associated with the game beingplayed on the other client device.

FIG. 5 depicts an example human-to-computer dialog between the user 101and an instance of automated assistant 120 operating on a client device406 carried by user 101. In this example, user 101 does not provide anatural language input. Instead, computing device 210 (once again takingthe form of a standalone interactive speaker) is playing music. Thismusic is detected at one or more audio sensors (e.g., microphones) ofclient device 406. One or more components of client device 406, such asa software application configured to analyze audibly-detected music, mayidentify one or more attributes of the detected music, such asartist/song/etc. Another component, such as entity module 134 in FIG. 1,may use these attributes to search one or more online sources (e.g.,knowledge graph 124) to identify the entity and related facts. Automatedassistant 120 operating on client device 406 may then provide (at 582)unsolicited content—e.g., out loud or visually on client device 406 inFIG. 5—that informs user 101 of various information about the entity.For example, at 582 of FIG. 5, automated assistant 120 states, “Did seeyou are listening to <artist>. Did you know that <artist> has a tourdate in <your town> on <date>?” Similar techniques may be applied by aninstance of automated assistant 120 operating on a client device (e.g.,a smart phone, tablet, laptop, standalone interactive speaker) when itdetects (via sound and or visual detection) audio-visual content (e.g.,movie, television program, sports event, etc.) being presented on auser's television.

FIGS. 2-5 depict human-to-computer dialogs in which a user 101 engageswith automated assistant 120 using audio input/output. However, this isnot meant to be limiting. As noted above, in various implementations,users may engage automated assistants using other means, such as messageexchange clients 107. FIG. 6 depicts an example in which a client device606 in the form of a smart phone or tablet (but that is not meant to belimiting) includes a touchscreen 640. Rendered visually on touchscreen640 is a transcript 642 of a human-to-computer dialog between a user(“You” in FIG. 6) of client device 606 and an instance of automatedassistant 120 executing (at least in part) on client device 606. Alsoprovided is an input field 644 in which the user is able to providenatural language content, as well as other types of inputs such asimages, sound, etc.

In FIG. 6, the user initiates the human-to-computer dialog session withthe question, “What time does <store> open?” Automated assistant 120(“AA” in FIG. 6) performs one or more searches for information relatedto the store's hours, and replies, “<store> opens at 10:00 AM.”Automated assistant 120 then determines one or more facts about theentity <store>, ranks those facts by measures of potential interest tothe user, and proactively incorporates the following content into thehuman-to-computer dialog: “By the way, <store> is having a Springclearance sale next month.” Automated assistant 120 then incorporates auser interface element in the form of a “card” 646 relating to the sale.Card 646 may include various content, such as a link to the store'swebsite, a so-called “deeplink” to a shopping application installed onclient device 606 that is operable to purchase items from store, etc. Insome implementations, automated assistant 120 may incorporate otherunsolicited content as selectable options as well, such as one or morehyperlinks 648 to webpages, e.g., for webpages associated with the storeand/or a competitor's webpage.

While the card 646 in FIG. 6 is a visual element that can be selected bytapping or otherwise touching it, this is not meant to be limiting. Asimilar human-to-computer dialog as that depicted in FIG. 6 could takeplace audibly between a user and an audio output device (e.g., thestandalone interactive speaker depicted in previous figures). In somesuch implementations, the user interface element may instead take theform of an audible prompt such as a question or option that may be“selected” if answered in the affirmative by the user. For example,instead of presenting the card 646, automated assistant 120 may audiblyoutput something like “Let me know if you would like me to list itemsthat will be on sale. In some implementations, the shopping applicationitself may include its own automated assistant that is tailoredspecifically to engage in a human-to-computer dialog with users toenable the user to learn about items for sale. In some suchimplementations, the user may be “passed on” to the shoppingapplication-specific automated assistant. In other implementations,automated assistant 120 may utilize various information and statesassociated with a shopping application to formulate natural languageoutput that solicits, from the user, information needed to procure itemsusing the shopping application. Automated assistant 120 may theninteract with the shopping application on behalf of the user (e.g., inresponse to spoken natural language inputs provided by the user).

FIG. 7 once again depicts client device 606 with touchscreen 640 anduser input field 644, as well as a transcript 742 of a human-to-computerdialog session. In this example, the user (“You”) initiates thehuman-to-computer dialog by typing and/or speaking (which may berecognized and converted to text) the natural language input, “Songs by<artist>.” Automated assistant 120 (“AA”) responds by providing a seriesof selectable user interface elements 746, any one of which the user mayselect (e.g., tap) to cause a corresponding song of the artist to play,e.g., on client device 606 or on another nearby client device (notdepicted, e.g., a standalone interactive speaker forming part of thesame coordinated ecosystem of client devices as 606). Using techniquesdescribed herein, automated assistant 120 determines—e.g., fromknowledge graph 124—that the artist turned forty last week. Accordingly,automated assistant 120 proactively incorporates the following statementinto the human-to-computer-dialog: “Did you know that <artist> turned 40last week?” In some implementations, if the user were to reply in theaffirmative or not reply at all, automated assistant 120 may refrainfrom providing additional proactive content, e.g., under the assumptionthat the user is not interested in receiving additional content.However, suppose the user were to provide a natural language responsethat suggests user interest, such as “really? I didn't know that! I feelold!” In such a case, automated assistant 120 may detect such asentiment (e.g., by performing sentiment analysis on the user's input)and provide additional fact(s) about the artist, or perhaps aboutanother closely related artist (e.g., an artist of a similar age).

The examples of proactively-incorporated unsolicited content describedabove are not meant to be limiting. Other unsolicited content ofpotential interest to users may be proactively incorporated intohuman-to-computer dialogs using techniques described herein. Forexample, in some implementations in which a user mentions an upcomingscheduled flight (or train departure or other travel arrangement),automated assistant 120 may proactively incorporate unsolicited contentinto a human-to-computer dialog session with the user. This unsolicitedcontent may include, for instance, an indication of traffic patterns onroute to the airport, one or more user interface elements that areselectable (by touch, voice, gesture, etc.) to open an application thatenables the user to view or edit the scheduled flight, information about(or selectable user interface elements that link to) alternative flightsthat may be less expensive, etc.

Of course, a user may not always desire unsolicited content. Forexample, a user may be driving in heavy traffic, may be in an emergencysituation, may be operating a computing device in a manner that suggeststhe user would not want to receive unsolicited content (e.g., in a videocall), etc. Accordingly, in some implementations, automated assistant120 may be configured to determine (e.g., based on signals such as alocation signal, context of a conversation, states of one or moreapplications, accelerometer signal, sentiment analysis of a user'snatural language input, etc.) a measure of desirability by the user toreceive unsolicited content, and may only provide unsolicited content ifthis measure satisfies one or more thresholds.

FIG. 8 once again depicts client device 606 with touchscreen 640 anduser input field 644, as well as a transcript 842 of a human-to-computerdialog session. In this example, the user (“You”) initiates thehuman-to-computer dialog by typing and/or speaking (which may berecognized and converted to text) the natural language input, “Flightsto Louisville next Monday.” Automated assistant 120 (“AA”) responds byproviding a series of selectable user interface elements 846, any one ofwhich the user may select (e.g., tap) to engage in a dialog, e.g., withautomated assistant or with a travel application installed on clientdevice 606, to procure the corresponding ticket. Using techniquesdescribed herein, automated assistant 120 also determines—e.g., by wayof proactive content module 136—that a less expensive flight isavailable if the user elects to leave next Tuesday, instead of nextMonday. Accordingly, automated assistant 120 proactively incorporatesthe following statement into the human-to-computer-dialog: “By the way,if you fly a day later, you can save $145.”

FIG. 9 is a flowchart illustrating an example method 900 according toimplementations disclosed herein. For convenience, the operations of theflow chart are described with reference to a system that performs theoperations. This system may include various components of variouscomputer systems, such as one or more components of automated assistant120. Moreover, while operations of method 900 are shown in a particularorder, this is not meant to be limiting. One or more operations may bereordered, omitted or added.

At block 902, the system may identify, e.g., by way of entity module 134based on content of an existing human-to-computer dialog session betweena user and an automated assistant, an entity mentioned by the user orautomated assistant. As alluded to above, entities may come in a varietyof forms, such as people (e.g., celebrities, public figures, authors,artists, etc.), places (cities, states, countries, points of interest,intersections, restaurants, businesses, hotels, etc.), things (e.g.,flights, train trips, products, services, songs, albums, films, books,poems, etc.), and so forth. Entity module 134 (or more generally,automated assistant 120) may identify entities using various datasources, such as knowledge graph 124, annotations from an entity taggerassociated with natural language processor 122, fresh content module138, other miscellaneous domain modules 140, and so forth.

At block 904, the system may identify, by way of entity module 134 basedon entity data contained in one or more databases, one or more factsrelated to the entity or to another entity that is related to theentity. For example, entity module 134 may consult knowledge graph 124for nodes, attributes, edges (which may represent relationships to otherentities), etc., that enable entity module 134 or another component ofautomated assistant 120 to identify facts about either the entity thatwas mentioned or another entity that is related to the mentioned entityin some way. For example, if the user or automated assistant mentionsMozart, automated assistant 120 may identify, in addition to or insteadof fact(s) associated with Mozart, fact(s) associated with anothersimilar composer of the same or similar era.

In some implementations, automated assistant 120 may rely on dataassociated with a user profile to identify facts associated with theentity and/or another entity. For example, suppose user mentions (e.g.,asks a question about, requests playback of a song composed by) a firstmusician in a human-to-computer dialog with automated assistant 120.Suppose further that the first musician is the user's most frequentlylistened-to musician (e.g., determined by playlists or playback historyassociated with the user's user profile), followed closely by a secondartist that the user also frequently listens to. In someimplementations, at block 904, automated assistant 120 may identifyfact(s) related to the first musician and/or fact(s) related to thesecond musician.

At block 906, the system may determine, for each of the one or morefacts determined at block 904, a corresponding measure of potentialinterest to the user (i.e., score the facts). Measures of potentialinterest may come in various forms (e.g., percentages, values along arange, numeric value, etc.) and may be determined in various ways. Insome implementations, measures of potential interest may be determinedbased on the user's own user profile. For example, if the userfrequently searches for flights from two different airports to comparecosts, and then mentions the first airport in a query to automatedassistant 120 about flights, automated assistant 120 may assign arelatively large measure of potential interest to (e.g., promote) factsrelated to flights from the second airport, even if the user didn'texplicitly mention the second airport.

Additionally or alternatively, in various implementations, the facts maybe scored using aggregate user data and/or behavior. For example, onlineconversations may be scraped to determine which entity attributes areoften raised, e.g., as asides, when an entity is discussed. As anotherexample, aggregate user search query logs may be analyzed to determinewhat entity attributes are often searched for, clicked, or otherwiseinteracted with when users search for or otherwise consume informationabout entities. As yet another example, in some implementations, thesystem may analyze trending searches and/or news, e.g., from freshcontent module 138, to determine which facts about entities may betrending now (and hence may be assigned greater measures of potentialinterest than they would have otherwise). As yet another example, auser's own contextual information (e.g., locational data generated by aposition coordinate sensor integral with the user's smart phone) may beused to assign measures of potential interest. For example, if adiscussed entity has an upcoming event nearby (as determined from theuser's current location), that fact may be assigned a greater measure ofpotential interest than if the only upcoming events related to theentity were in faraway locations.

At block 908, the system may generate unsolicited natural languagecontent that includes one or more of the facts selected based on thecorresponding one or more measures of potential interest. In someimplementations, the system may select only the top-ranked fact to beincluded in the unsolicited content. In other implementations, thesystem may select the top n (positive integer) ranked facts. At block910, the system may incorporate, into the existing human-to-computerdialog session or a subsequent human-to-computer dialog session, theunsolicited natural language content. For example, automated assistant120 may generate a natural language statement that is prefaced by aphrase such as “By the way,” “Did you know,” “As a side note,” etc. Asdescribed above, in some implementations, this unsolicited content mayinclude and/or be accompanied by selectable graphical elements, such asdeep links, that a user may select to engage in additional dialog,initiate various tasks, etc.

While not depicted explicitly in FIG. 9, in various implementations,automated assistant 120 may avoid incorporating unsolicited content thatmay already be known to the user into a human-to-computer dialog sessionwith a user. For example, in some implementations, the system may detectthat a given fact of one or more facts identified at block 904 has beenpreviously referenced in the existing human-to-computer dialog betweenthe user and the automated assistant or in a previous human-to-computerdialog between the user and the automated assistant (in some cases goingback a predetermined time interval, such as thirty days). In someimplementations, the measure of potential interest determined by thesystem for the given fact may reflect this detecting, e.g., by beingassigned a relatively low score (even no score in some cases). In otherimplementations, the system may eliminate a given fact fromconsideration based on detecting that the given fact has been previouslyreferenced in the existing human-to-computer dialog between the user andthe automated assistant or in a previous human-to-computer dialogbetween the user and the automated assistant.

FIG. 10 is a block diagram of an example computing device 1010 that mayoptionally be utilized to perform one or more aspects of techniquesdescribed herein. In some implementations, one or more of a clientcomputing device, automated assistant 120, and/or other component(s) maycomprise one or more components of the example computing device 1010.

Computing device 1010 typically includes at least one processor 1014which communicates with a number of peripheral devices via bus subsystem1012. These peripheral devices may include a storage subsystem 1024,including, for example, a memory subsystem 1025 and a file storagesubsystem 1026, user interface output devices 1020, user interface inputdevices 1022, and a network interface subsystem 1016. The input andoutput devices allow user interaction with computing device 1010.Network interface subsystem 1016 provides an interface to outsidenetworks and is coupled to corresponding interface devices in othercomputing devices.

User interface input devices 1022 may include a keyboard, pointingdevices such as a mouse, trackball, touchpad, or graphics tablet, ascanner, a touchscreen incorporated into the display, audio inputdevices such as voice recognition systems, microphones, and/or othertypes of input devices. In general, use of the term “input device” isintended to include all possible types of devices and ways to inputinformation into computing device 1010 or onto a communication network.

User interface output devices 1020 may include a display subsystem, aprinter, a fax machine, or non-visual displays such as audio outputdevices. The display subsystem may include a cathode ray tube (CRT), aflat-panel device such as a liquid crystal display (LCD), a projectiondevice, or some other mechanism for creating a visible image. Thedisplay subsystem may also provide non-visual display such as via audiooutput devices. In general, use of the term “output device” is intendedto include all possible types of devices and ways to output informationfrom computing device 1010 to the user or to another machine orcomputing device.

Storage subsystem 1024 stores programming and data constructs thatprovide the functionality of some or all of the modules describedherein. For example, the storage subsystem 1024 may include the logic toperform selected aspects of the method of FIG. 9, as well as toimplement various components depicted in FIG. 1.

These software modules are generally executed by processor 1014 alone orin combination with other processors. Memory 1025 used in the storagesubsystem 1024 can include a number of memories including a main randomaccess memory (RAM) 1030 for storage of instructions and data duringprogram execution and a read only memory (ROM) 1032 in which fixedinstructions are stored. A file storage subsystem 1026 can providepersistent storage for program and data files, and may include a harddisk drive, a floppy disk drive along with associated removable media, aCD-ROM drive, an optical drive, or removable media cartridges. Themodules implementing the functionality of certain implementations may bestored by file storage subsystem 1026 in the storage subsystem 1024, orin other machines accessible by the processor(s) 1014.

Bus subsystem 1012 provides a mechanism for letting the variouscomponents and subsystems of computing device 1010 communicate with eachother as intended. Although bus subsystem 1012 is shown schematically asa single bus, alternative implementations of the bus subsystem may usemultiple busses.

Computing device 1010 can be of varying types including a workstation,server, computing cluster, blade server, server farm, or any other dataprocessing system or computing device. Due to the ever-changing natureof computers and networks, the description of computing device 1010depicted in FIG. 10 is intended only as a specific example for purposesof illustrating some implementations. Many other configurations ofcomputing device 1010 are possible having more or fewer components thanthe computing device depicted in FIG. 10.

In situations in which certain implementations discussed herein maycollect or use personal information about users (e.g., user dataextracted from other electronic communications, information about auser's social network, a user's location, a user's time, a user'sbiometric information, and a user's activities and demographicinformation, relationships between users, etc.), users are provided withone or more opportunities to control whether information is collected,whether the personal information is stored, whether the personalinformation is used, and how the information is collected about theuser, stored and used. That is, the systems and methods discussed hereincollect, store and/or use user personal information only upon receivingexplicit authorization from the relevant users to do so.

For example, a user is provided with control over whether programs orfeatures collect user information about that particular user or otherusers relevant to the program or feature. Each user for which personalinformation is to be collected is presented with one or more options toallow control over the information collection relevant to that user, toprovide permission or authorization as to whether the information iscollected and as to which portions of the information are to becollected. For example, users can be provided with one or more suchcontrol options over a communication network. In addition, certain datamay be treated in one or more ways before it is stored or used so thatpersonally identifiable information is removed. As one example, a user'sidentity may be treated so that no personally identifiable informationcan be determined. As another example, a user's geographic location maybe generalized to a larger region so that the user's particular locationcannot be determined.

While several implementations have been described and illustratedherein, a variety of other means and/or structures for performing thefunction and/or obtaining the results and/or one or more of theadvantages described herein may be utilized, and each of such variationsand/or modifications is deemed to be within the scope of theimplementations described herein. More generally, all parameters,dimensions, materials, and configurations described herein are meant tobe exemplary and that the actual parameters, dimensions, materials,and/or configurations will depend upon the specific application orapplications for which the teachings is/are used. Those skilled in theart will recognize, or be able to ascertain using no more than routineexperimentation, many equivalents to the specific implementationsdescribed herein. It is, therefore, to be understood that the foregoingimplementations are presented by way of example only and that, withinthe scope of the appended claims and equivalents thereto,implementations may be practiced otherwise than as specificallydescribed and claimed. Implementations of the present disclosure aredirected to each individual feature, system, article, material, kit,and/or method described herein. In addition, any combination of two ormore such features, systems, articles, materials, kits, and/or methods,if such features, systems, articles, materials, kits, and/or methods arenot mutually inconsistent, is included within the scope of the presentdisclosure.

What is claimed is:
 1. A method implemented by one or more processors,comprising: identifying an entity based on a state of a media or gamingapplication operating on a first client device operated by a user,wherein the entity is identified without using explicit input from theuser; determining that an automated assistant operating on a secondclient device associated with the user has no outstanding obligations tothe user; identifying one or more facts about the entity based on entitydata contained in one or more databases; determining, for each of theone or more facts, a corresponding measure of potential interest to theuser; generating, by one or more of the processors, unsolicited naturallanguage content, wherein the unsolicited natural language contentincludes one or more of the facts selected based on the correspondingone or more measures of potential interest; and after the determinationthat the automated assistant has no outstanding obligations to the user,incorporating, by the automated assistant into a new or existinghuman-to-computer dialog session between the user and the automatedassistant, the unsolicited natural language content, wherein theincorporating causes the unsolicited natural language content to beautomatically output to the user as part of the new or existinghuman-to-computer dialog session.
 2. The method of claim 1, whereindetermining the corresponding measure of potential interest is based ondata associated with a user profile associated the user.
 3. The methodof claim 2, wherein the data associated with the user profile includes asearch history associated with the user.
 4. The method of claim 1,wherein determining the corresponding measure of potential interest isbased on analysis of a corpus of one or more online conversationsbetween one or more people.
 5. The method of claim 4, wherein theanalysis includes detection of one or more references to one or moreentities, as well as one or references to facts about the one or moreentities within a particular proximity of the one or more references tothe one or more entities.
 6. The method of claim 5, wherein the one ormore entities include the entity determined based on the state of themedia or gaming application.
 7. The method of claim 5, wherein the oneor more entities share an entity class with the entity determined basedon the state of the media or gaming application.
 8. The method of claim5, wherein the one or more entities share one or more attributes withthe entity determined based on the state of the media or gamingapplication.
 9. The method of claim 1, wherein determining thecorresponding measure of potential interest comprises detecting that agiven fact of the one or more facts has been previously referenced inthe existing human-to-computer dialog session between the user and theautomated assistant or in a previous human-to-computer dialog sessionbetween the user and the automated assistant, wherein the measure ofpotential interest determined for the given fact reflects the detecting.10. The method of claim 1, further comprising eliminating, by one ormore of the processors, a given fact of the one or more facts fromconsideration based on detecting that the given fact has been previouslyreferenced in the existing human-to-computer dialog session between theuser and the automated assistant or in a previous human-to-computerdialog session between the user and the automated assistant.
 11. Themethod of claim 1, wherein the entity relates to a video game, and agiven fact of the one or more facts is a fact related to the video game.12. The method of claim 1, wherein the entity is a person, and a givenfact of the one or more facts is an upcoming event involving the person.13. A system comprising one or more processors and memory operablycoupled with the one or more processors, wherein the memory storesinstructions that, in response to execution of the instructions by oneor more processors, cause the one or more processors to perform thefollowing operations: identifying an entity based on a state of a mediaor gaming application operating on a first client device operated by auser, wherein the entity is identified without using explicit input fromthe user; determining that an automated assistant operating on a secondclient device associated with the user has no outstanding obligations tothe user; identifying one or more facts about the entity based on entitydata contained in one or more databases; determining, for each of theone or more facts, a corresponding measure of potential interest to theuser; generating, by one or more of the processors, unsolicited naturallanguage content, wherein the unsolicited natural language contentincludes one or more of the facts selected based on the correspondingone or more measures of potential interest; and after the determinationthat the automated assistant has no outstanding obligations to the user,incorporating, by the automated assistant into a new or existinghuman-to-computer dialog session between the user and the automatedassistant, the unsolicited natural language content, wherein theincorporating causes the unsolicited natural language content to beautomatically output to the user as part of the new or existinghuman-to-computer dialog session.
 14. The system of claim 13, whereindetermining the corresponding measure of potential interest is based ondata associated with a user profile associated the user.
 15. The systemof claim 14, wherein the data associated with the user profile includesa search history associated with the user.
 16. The system of claim 13,wherein determining the corresponding measure of potential interest isbased on analysis of a corpus of one or more online conversationsbetween one or more people.
 17. The system of claim 16, wherein theanalysis includes detection of one or more references to one or moreentities, as well as one or references to facts about the one or moreentities within a particular proximity of the one or more references tothe one or more entities.
 18. The system of claim 17, wherein the one ormore entities include the entity determined based on the state of themedia or gaming application.
 19. The system of claim 13, wherein theentity relates to a video game, and a given fact of the one or morefacts is a fact related to the video game.
 20. At least onenon-transitory computer-readable medium comprising instructions that, inresponse to execution of the instructions by one or more processors,cause the one or more processors to perform the following operations:identifying an entity based on a state of a media or gaming applicationoperating on a first client device operated by a user, wherein theentity is identified without using explicit input from the user;determining that an automated assistant operating on a second clientdevice associated with the user has no outstanding obligations to theuser; identifying one or more facts about the entity based on entitydata contained in one or more databases; determining, for each of theone or more facts, a corresponding measure of potential interest to theuser; generating, by one or more of the processors, unsolicited naturallanguage content, wherein the unsolicited natural language contentincludes one or more of the facts selected based on the correspondingone or more measures of potential interest; and after the determinationthat the automated assistant has no outstanding obligations to the user,incorporating, by the automated assistant into a new or existinghuman-to-computer dialog session between the user and the automatedassistant, the unsolicited natural language content, wherein theincorporating causes the unsolicited natural language content to beautomatically output to the user as part of the new or existinghuman-to-computer dialog session.